Automated image segmentation-assisted flattening of atomic force microscopy images

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li and
• Huimin Wang

Beilstein J. Nanotechnol. 2018, 9, 975–985, doi:10.3762/bjnano.9.91

• . Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method. Keywords: atomic force microscopy; contour expansion; image flattening; polynomial fitting; sliding window; Introduction Since its invention, the atomic

• , whereby methods including thresholding [31][32], circle Hough transform [33], and clustering [30] can be applied. Recently, Wang et al. proposed a contour expansion method for feature extraction in AFM height images [3][34]. The method achieves an accurate localization and optimized boundary detection for

• lines can no longer be represented by polynomial curves. To solve the problems mentioned above, a two-step scheme was proposed for optimized flattening of AFM images in this study. In this method, the contour expansion method [3] was first applied to achieve automated extraction of exclusion mask areas

Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li,
• Shuai Ren and
• Shusheng Bi

Beilstein J. Nanotechnol. 2017, 8, 2572–2582, doi:10.3762/bjnano.8.257

• . After that, the so-called contour expansion operation was applied to achieve optimized boundaries. The principle and the detailed procedure of the proposed method were presented, followed by the demonstration of the automated segmentation and morphological characterization. The result shows that the

• footprint height, even for the adaptive thresholding method. To solve the problem, we have proposed a contour expansion method [27]. In this method, AFM images were first preliminarily detected using the thresholding method. The active contour method is then applied to the boundaries of the preliminarily

• extracted. Then, the contour expansion method [27] was applied to the initial contours to get the optimized boundary detection based on the active contour model [29], followed by the morphological characterization. Experimental Imaging of nanobubbles and nanodroplets In this study, NBs were produced on a PS

Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence

• Yuliang Wang,
• Huimin Wang,
• Shusheng Bi and
• Bin Guo

Beilstein J. Nanotechnol. 2015, 6, 952–963, doi:10.3762/bjnano.6.98

• contour expansion results with different threshold values during contour initialization. The blue contours in Figure 7a,b are initialized contours with threshold values of 10 nm and 18 nm, respectively. The green contour in Figure 7a and the purple contour in Figure 7b are converged contours using the